an online book by David Gurteen
Abductive reasoning is the process of forming a likely explanation or hypothesis from an incomplete set of observations. It involves inferring the most plausible cause for the observed evidence, often used when the information is insufficient for deductive or inductive reasoning.
Abductive reasoning (also called abduction, abductive inference, or retroduction) is a form of logical inference that seeks the simplest and most likely conclusion from a set of observations. It was formulated and advanced by American philosopher and logician Charles Sanders Peirce beginning in the latter half of the 19th century.
Abductive reasoning, unlike deductive reasoning, yields a plausible conclusion but does not definitively verify it. Abductive conclusions do not eliminate uncertainty or doubt, which is expressed in terms such as "best available" or "most likely". While inductive reasoning draws general conclusions that apply to many situations, abductive conclusions are confined to the particular observations in question.
In the 1990s, as computing power grew, the fields of law, computer science, and artificial intelligence research spurred renewed interest in the subject of abduction. Diagnostic expert systems frequently employ abduction.
Credit: Wikipedia - abductive_reasoningMaking sound conclusions is crucial. Distinguishing between deductive, inductive, and abductive reasoning clarifies how we derive these conclusions. Understanding these reasoning forms enhances logical thinking and decision-making. Continue reading Three Forms of Reasoning Understanding deductive, inductive, and abductive logic
